Jae-Joon Han

Computer Vision and Pattern Recognition · with Son · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 176 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 81 citations into it
• Artificial Intelligence — 60 citations into it
• Efficiency & Compression — 28 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 7 citations into it
• Signal Processing — 7 citations into it

Whom Jae-Joon Han trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ross B. Girshick
6. Ilya Sutskever
7. Karen Simonyan
8. Yann LeCun
9. Diederik P. Kingma
10. X. Zhang

Papers from Jae-Joon Han's perspective

Ordered by how strongly the researchers Jae-Joon Han trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. ImageNet Large Scale Visual Recognition Challenge
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Going deeper with convolutions
10. Long Short-Term Memory

Their papers

• Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss (2018) — 430 citations
• Quality-Agnostic Image Recognition via Invertible Decoder (2021) — 28 citations
• Joint Training of Low-Precision Neural Network with Quantization Interval Parameters (2018) — 36 citations